Personalised Clinical Decision Support For Diabetes Management Using Real-time Data [Pòster]

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Martin, Clare
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Aldea, Arantza
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Brown, Daniel
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Duce, David
orcId Fernández-Real Lemos, José Manuel​ ​ ​ ​
Fernández-Real Lemos, José Manuel
orcId Gay Sacristán, Pablo​ ​ scopusId Gay Sacristán, Pablo​ ​
Gay Sacristán, Pablo
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Georgiou, Pantelis
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Harrison, R.
orcId Herrero i Viñas, Pau​ ​ ​ ​
Herrero i Viñas, Pau
orcId Innocenti, Bianca​ ​ scopusId Innocenti, Bianca​ ​
Innocenti, Bianca
orcId López Ibáñez, Beatriz​ researcherId López Ibáñez, Beatriz​ scopusId López Ibáñez, Beatriz​ ​
López Ibáñez, Beatriz
​ researcherId Leal Moncada, Yenny Teresa​ scopusId Leal Moncada, Yenny Teresa​ ​
Leal Moncada, Yenny Teresa
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Nita, Lucian
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Pesl, Peter
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Petite, Roberto
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Reddy, Monika
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Shapley, Julian
orcId Torrent-Fontbona, Ferran​ researcherId Torrent-Fontbona, Ferran​ scopusId Torrent-Fontbona, Ferran​ ​
Torrent-Fontbona, Ferran
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Waite, Marion
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Wos, Marzena
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Oliver, Nick
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PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded research project to develop a personalised clinical decision support system for Type 1 diabetes self-management. The tool provides insulin bolus dose advice and carbohydrate recommendations, tailored to the needs of individuals. The former is determined by Case-Based Reasoning (CBR), an artificial intelligence technique that adapts to new situations according to past experience. The latter uses a predictive computer model that also promotes safety by providing glucose alarms, low-glucose insulin suspension and fault detection ​
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